Short-term Time Series Forecasting with Regression Automata

We present regression automata (RA), which are novel type
syntactic models for time series forecasting. Building on
top of conventional state-merging algorithms for identifying
automata, RA use numeric data in addition to symbolic
values and make predictions based on this data in a regression
fashion. We apply our model to the problem of hourly
wind speed and wind power forecasting. Our results show
that RA outperform other state-of-the-art approaches for
predicting both wind speed and power generation. In both
cases, short-term predictions are used for resource allocation
and infrastructure load balancing. For those critical tasks,
the ability to inspect and interpret the generative model RA
provide is an additional benefit.